As organizations have become more reliant on computers for performing day to day activities, so too has the reliance on networks and information technology (IT) infrastructures increased. It is well known that large organizations utilize distributed computing systems connected locally over local area networks (LAN) and across the geographical areas through wide-area networks (WAN).
While the benefits of a distributed approach are numerous and well understood, there has arisen significant practical challenges in managing such environments. Decentralized control and decision making around capacity, the perception that the cost of adding systems is inexpensive, and the increased popularity of server virtualization technologies, have created environments comprised of a multitude of interconnected systems with excess capacity.
Too many servers result in extra costs, mostly through additional capital, maintenance and upgrade expenses; redundant software licenses; and excess heat production and power consumption. As such, removing even a modest number of servers from a large IT infrastructure can save a significant amount of cost on a yearly basis.
Organizations concerned with such redundancies need to determine how they can best achieve consolidation of capacity. In general, consolidating systems is a daunting task as there are many possible consolidation combinations with varying levels of benefits and risks.
To determine the most suitable consolidation solution, understanding dependencies and relationships between systems is critical. System and application dependencies are typically determined through non-empirical methods such as the inspection of detailed configuration and run-time settings of the systems and applications combined with domain knowledge of the computing environment.